Optimizing Student Extracurricular Classification: RapidMiner Based K-Means Clustering Study at Darunnajah High School
Abstract
Abstract− This scientific journal discusses the grouping of students' extracurricular activities at Darunnajah School using the K-Means method based on RapidMiner. This research aims to uncover patterns and preferences in the realm of extracurricular involvement, providing valuable insights for optimizing school programs. Using the K-Means clustering technique with RapidMiner, groups of students with similar extracurricular interests can be identified. This research contributes to a deeper understanding of student engagement in non-academic activities, forming the basis for tailored planning and enhanced extracurricular offerings. The implementation of the RapidMiner platform has proven to be very efficient in analyzing and grouping student data, creating a more dynamic and interesting extracurricular environment at Darunnajah School.
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